{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T20:25:24Z","timestamp":1791923124884,"version":"4.3.4"},"reference-count":32,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objective: A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification\/detection of B-lines to help clinicians in assessing pulmonary conditions. Methods: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 \u00b1 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. Results: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. Conclusions: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection\/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses.<\/jats:p>","DOI":"10.3390\/diagnostics14222526","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T06:28:32Z","timestamp":1731392912000},"page":"2526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines"],"prefix":"10.3390","volume":"14","author":[{"given":"Mahdiar","family":"Nekoui","sequence":"first","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Ehsan","family":"Seyed Bolouri","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Forouzandeh","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masood","family":"Dehghan","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dornoosh","family":"Zonoobi","sequence":"additional","affiliation":[{"name":"Exo Imaging, Santa Clara, CA 95054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5314-2297","authenticated-orcid":false,"given":"Jacob L.","family":"Jaremko","sequence":"additional","affiliation":[{"name":"Department of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Buchanan","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, University of Alberta, Edmonton, AB T6G 2R3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Nagdev","sequence":"additional","affiliation":[{"name":"Alameda Health System, Highland General Hospital, University of California San Francisco, San Francisco, CA 94143, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeevesh","family":"Kapur","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, National University of Singapore, Singapore 119228, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Komatsu, M., Sakai, A., Dozen, A., Shozu, K., Yasutomi, S., Machino, H., Asada, K., Kaneko, S., and Hamamoto, R. (2021). Towards clinical application of artificial intelligence in ultrasound imaging. Biomedicines, 9.","DOI":"10.3390\/biomedicines9070720"},{"key":"ref_2","unstructured":"Loukas, M., and Burns, D. (2024). Essential Ultrasound Anatomy, Lippincott Williams & Wilkins."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1476-7120-9-6","article-title":"Lung ultrasound: A new tool for the cardiologist","volume":"9","author":"Gargani","year":"2011","journal-title":"Cardiovasc. Ultrasound"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Haji-Hassan, M., Lenghel, L.M., and Bolboac\u0103, S.D. (2021). Hand-held ultrasound of the lung: A systematic review. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081381"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1164\/rccm.201802-0236CI","article-title":"Lung ultrasound for critically ill patients","volume":"199","author":"Mojoli","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pelosi, P., Tonelli, R., Torregiani, C., Baratella, E., Confalonieri, M., Battaglini, D., Marchioni, A., Confalonieri, P., Clini, E., and Salton, F. (2022). Different methods to improve the monitoring of noninvasive respiratory support of patients with severe pneumonia\/ARDS due to COVID-19: An update. J. Clin. Med., 11.","DOI":"10.3390\/jcm11061704"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1186\/s13075-024-03399-2","article-title":"A versatile role for lung ultrasound in systemic autoimmune rheumatic diseases related pulmonary involvement: A narrative review","volume":"26","author":"Wang","year":"2024","journal-title":"Arthritis Res. Ther."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chiumello, D., Coppola, S., Catozzi, G., Danzo, F., Santus, P., and Radovanovic, D. (2024). Lung Imaging and Artificial Intelligence in ARDS. J. Clin. Med., 13.","DOI":"10.3390\/jcm13020305"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s13665-024-00344-1","article-title":"Artificial Intelligence in Lung Ultrasound","volume":"13","author":"Chu","year":"2024","journal-title":"Curr. Pulmonol. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100206","DOI":"10.1016\/j.opresp.2022.100206","article-title":"The Role of Lung Ultrasound in the Management of Respiratory Emergencies","volume":"4","author":"Pazos","year":"2022","journal-title":"Open Respir. Arch."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1378\/chest.09-0001","article-title":"A-lines and B-lines: Lung ultrasound as a bedside tool for predicting pulmonary artery occlusion pressure in the critically ill","volume":"136","author":"Lichtenstein","year":"2009","journal-title":"Chest"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"875","DOI":"10.3390\/ai4040044","article-title":"Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound","volume":"4","author":"Sultan","year":"2023","journal-title":"AI"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"45","DOI":"10.11152\/mu-1885","article-title":"Visual versus automatic ultrasound scoring of lung B-lines: Reliability and consistency between systems","volume":"21","author":"Short","year":"2019","journal-title":"Med Ultrason."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TUFFC.2018.2885955","article-title":"Automatic detection of B-lines in In Vivo lung ultrasound","volume":"66","author":"Moshavegh","year":"2018","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Panisello-Tafalla, A., Haro-Montoya, M., Caballol-Angelats, R., Montelongo-Sol, M., Rodriguez-Carralero, Y., Lucas-Noll, J., and Clua-Espuny, J.L. (2024). Prognostic Significance of Lung Ultrasound for Heart Failure Patient Management in Primary Care: A Systematic Review. J. Clin. Med., 13.","DOI":"10.20944\/preprints202403.1376.v1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10140-008-0701-x","article-title":"Usefulness of lung ultrasound in the bedside distinction between pulmonary edema and exacerbation of COPD","volume":"15","author":"Volpicelli","year":"2008","journal-title":"Emerg. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1186\/s13054-021-03770-8","article-title":"Feasibility of machine integrated point of care lung ultrasound automatic B-lines tool in the Corona-virus 2019 critical care unit","volume":"25","author":"Tsaban","year":"2021","journal-title":"Crit. Care"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s00134-012-2513-4","article-title":"International evidence-based recommendations for point-of-care lung ultrasound","volume":"38","author":"Volpicelli","year":"2012","journal-title":"Intensive Care Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1093\/ndt\/gfw329","article-title":"Efficacy of a remote web-based lung ultrasound training for nephrologists and cardiologists: A LUST trial sub-project","volume":"31","author":"Gargani","year":"2016","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_20","first-page":"17","article-title":"Artificial intelligence and democratization of the use of lung ultrasound in COVID-19: On the feasibility of automatic calculation of lung ultrasound score","volume":"2","author":"Camacho","year":"2022","journal-title":"Int. J. Transl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Marozzi, M.S., Cicco, S., Mancini, F., Corvasce, F., Lombardi, F.A., Desantis, V., Loponte, L., Giliberti, T., Morelli, C.M., and Longo, S. (2024). A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study. Diagnostics, 14.","DOI":"10.3390\/diagnostics14020155"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107251","DOI":"10.1016\/j.ultras.2024.107251","article-title":"Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound","volume":"140","author":"Howell","year":"2024","journal-title":"Ultrasonics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1186\/s13054-023-04548-w","article-title":"Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit","volume":"27","author":"Nhat","year":"2023","journal-title":"Critical Care"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1097\/01.NPR.0000841944.00536.b2","article-title":"Point-of-care ultrasound: An emerging clinical tool to enhance physical assessment","volume":"47","author":"Fraleigh","year":"2022","journal-title":"Nurse Pract."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.25259\/JCIS_29_2019","article-title":"Lung ultrasound volume sweep imaging for pneumonia detection in rural areas: Piloting training in rural Peru","volume":"9","author":"Marini","year":"2019","journal-title":"J. Clin. Imaging Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Peng, J., Xu, J., and Bouridane, A. (2023). Survey of explainable AI techniques in healthcare. Sensors, 23.","DOI":"10.3390\/s23020634"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1109\/TMI.2017.2715880","article-title":"Line detection as an inverse problem: Application to lung ultrasound imaging","volume":"36","author":"Anantrasirichai","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kuroda, Y., Kaneko, T., Yoshikawa, H., Uchiyama, S., Nagata, Y., Matsushita, Y., Hiki, M., Minamino, T., Takahashi, K., and Daida, H. (2023). Artificial intelligence-based point-of-care lung ultrasound for screening COVID-19 pneumoniae: Comparison with CT scans. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0281127"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13089-021-00234-6","article-title":"B-line quantification: Comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review","volume":"13","author":"Russell","year":"2021","journal-title":"Ultrasound J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115","DOI":"10.7863\/jum.2013.32.1.115","article-title":"Inter-rater reliability of quantifying pleural B-lines using multiple counting methods","volume":"32","author":"Anderson","year":"2013","journal-title":"J. Ultrasound Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Herraiz, J.L., Freijo, C., Camacho, J., Mu\u00f1oz, M., Gonz\u00e1lez, R., Alonso-Roca, R., \u00c1lvarez-Troncoso, J., Beltr\u00e1n-Romero, L.M., Bernabeu-Wittel, M., and Blancas, R. (2023). Inter-rater variability in the evaluation of lung ultrasound in videos acquired from COVID-19 patients. Appl. Sci., 13.","DOI":"10.3390\/app13031321"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1016\/j.annemergmed.2021.07.020","article-title":"19 Automated Quantification Of B-Lines in Lung Ultrasound On COVID-19 Patients","volume":"78","author":"Baloescu","year":"2021","journal-title":"Ann. Emerg. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/22\/2526\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:30:40Z","timestamp":1760113840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/22\/2526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"references-count":32,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["diagnostics14222526"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14222526","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]}}}